Autocomplete Query Ranking with Reinforcement-Learned Intent Adaptation
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Solution Overview
Problem
Conventional autocomplete systems struggle to generate accurate queries when users input natural language prefixes with advanced semantic and lexical components, failing to understand user intent and requiring excessive user input.
Innovation Solution
A computing system utilizing a large language model trained with reinforcement learning to generate and rank autocomplete queries, incorporating user feedback and non-user based inputs to adapt to changing preferences and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lexical matching is used for autocomplete, then the system is simple to implement, but it fails to understand user intent and generate accurate queries for natural language prefixes
Solution Approach 1:
The patent replaces conventional lexical matching mechanisms with a large language model that uses reinforcement learning. This substitution enables the system to understand natural language prefixes and generate accurate autocomplete queries by learning from user feedback, rather than relying on simple keyword matching algorithms.
Solution Approach 2:
The system dynamically adjusts its behavior based on user feedback by modifying the reinforcement learning parameters. The large language model learns optimal query generation strategies through continuous interaction, changing its output parameters based on observed user preferences and behavior patterns.
2Measurement precision
If conventional autocomplete systems require more user input to generate accurate queries, then they may achieve better precision, but this increases the time and effort required from users
Solution Approach 1:
The large language model autonomously completes the query generation process by understanding natural language prefixes and generating relevant autocomplete suggestions without requiring additional user input. The system serves itself by using reinforcement learning to automatically adapt to user preferences and generate accurate queries from minimal input.
Solution Approach 2:
The system performs preliminary analysis of the natural language prefix using the large language model to understand user intent before generating autocomplete suggestions. This preliminary understanding allows the system to provide accurate suggestions early in the interaction, reducing the time users need to spend formulating their queries.
3Adaptability or versatility
If the system adapts to changing user preferences through reinforcement learning, then autocomplete accuracy improves, but the computational resources and training time increase
Solution Approach 1:
The system implements reinforcement learning by continuously receiving feedback from user interactions with autocomplete suggestions. This feedback loop allows the large language model to learn and adapt to changing user preferences over time, adjusting its query generation strategy based on observed user behavior and preferences.
Solution Approach 2:
The system dynamically adapts its behavior through reinforcement learning, allowing the large language model to change its query generation parameters in response to evolving user preferences. This dynamic adaptation enables the system to remain versatile and accurate as user behavior changes, rather than relying on static rules.
Data Source
AI summary
Techniques are described herein for generating an autocomplete query. An example method includes a system receiving, via graphical user interface (GUI) at a first device, a first prefix. The system can generate, via a first language model, a first autocomplete query associated with an item based at least in part on the first prefix. The system can determine a non-user based input for a reward model. The system can generate a reward for the first language model based at least in part on the non-user based input. The system can cause a change of weights of the first language model based at least in part on the reward. The system can receive, via a second GUI at a second device, the first autocomplete prefix. The system can generate a second autocomplete query based at least in part on the first autocomplete prefix and the change of weights.